Abstract

This paper introduces a new technique in the investigation of limited-dependent variable models. This paper illustrates that variable precision rough set theory (VPRS), allied with the use of a modern method of classification, or discretisation of data, can out-perform the more standard approaches that are employed in economics, such as a probit model. These approaches and certain inductive decision tree methods are compared (through a Monte Carlo simulation approach) in the analysis of the decisions reached by the UK Monopolies and Mergers Committee. We show that, particularly in small samples, the VPRS model can improve on more traditional models, both in-sample, and particularly in out-of-sample prediction. A similar improvement in out-of-sample prediction over the decision tree methods is also shown.
Scope and purpose
The Monopolies and Mergers Commission (MMC) in the UK evaluates whether a given firm, or set of firms is behaving in a manner that is considered to be against the public interest, that is anti-competitive. The interpretation and prediction of the decisions made by the MMC is of importance to firm's possible future investment plans. Through the construction of decision rules using the variable precision rough sets (VPRS) model this interoperation and prediction is able to be undertaken. The importance of the concomitant variables in the decisions made is shown through a ‘leave n out’ Monte Carlo simulation approach. At the technical level this study illustrates one of the first applications of VPRS in an economic environment.